arXiv 1 Apr 2022 · Statistics — Applications · publishedJournal of Econometrics (2024) · 1 citations (OpenAlex)
arXiv:2204.00180 · PDF · DOI · OpenAlex · Extracted main text
Diagnostic tests are almost never perfect. Studies quantifying their performance use knowledge of the true health status, measured with a reference diagnostic test. Researchers commonly assume that the reference test is perfect, which is often not the case in practice. When the assumption fails, conventional studies identify "apparent" performance or performance with respect to the reference, but not true performance. This paper provides the smallest possible bounds on the measures of true performance - sensitivity (true positive rate) and specificity (true negative rate), or equivalently false positive and negative rates, in standard settings. Implied bounds on policy-relevant parameters are derived: 1) Prevalence in screened populations; 2) Predictive values. Methods for inference based on moment inequalities are used to construct uniformly consistent confidence sets in level over a relevant family of data distributions. Emergency Use Authorization (EUA) and independent study data for the BinaxNOW COVID-19 antigen test demonstrate that the bounds can be very informative. Analysis reveals that the estimated false negative rates for symptomatic and asymptomatic patients are up to 3.17 and 4.59 times higher than the frequently cited "apparent" false negative rate. Further applicability of the results in the context of imperfect proxies such as survey responses and imputed protected classes is indicated.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Emerson, Sarah C, Waikar, Sushrut S, Fuentes, Claudio, Bonventre, Jo… (2018) Biomarker validation with an imperfect reference: Issues and bounds | 1.000 | 14 | 4 | 100% |
| 2 | Thibodeau, LA (1981) Evaluating diagnostic tests | 1.000 | 9 | 4 | 100% |
| 3 | Romano, Joseph P, Shaikh, Azeem M, Wolf, Michael (2014) A practical two-step method for testing moment inequalities | 1.000 | 7 | 3 | 100% |
| 4 | Gart, John J, Buck, Alfred A (1966) Comparison of a Screening Test and a Reference Test in Epidemiologic Studies: A Probabilistic Model for the Comparison of Diagno… | 1.000 | 5 | 3 | 100% |
| 5 | Shah, Melisa M, Salvatore, Phillip P, Ford, Laura, Kamitani, Emiko,… (2021) Performance of Repeat BinaxNOW Severe Acute Respiratory Syndrome Coronavirus 2 Antigen Testing in a Community Setting, Wisconsin… | 0.959 | 17 | 3 | 88% |
| 6 | Cross, Philip J, Manski, Charles F (2002) Regressions, short and long | 0.928 | 4 | 3 | 100% |
| 7 | Valenstein, Paul N (1990) Evaluating diagnostic tests with imperfect standards | 0.874 | 7 | 2 | 100% |
| 8 | (2022) Bounding infection prevalence by bounding selectivity and accuracy of tests: with application to early COVID-19 | 0.843 | 3 | 3 | 100% |
| 9 | Kanji, Jamil N, Zelyas, Nathan, MacDonald, Clayton, Pabbaraju, Kanti… (2021) False negative rate of COVID-19 PCR testing: a discordant testing analysis | 0.811 | 4 | 2 | 100% |
| 10 | Manski, Charles F (2020) Bounding the accuracy of diagnostic tests, with application to COVID-19 antibody tests | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 72 scored citations.